RED WINE EXPLORATION by PIERRE CONREAUX
Introduction
Our goal is to get a better understanding of which chemical properties influence the quality of red wines.
We will use a public dataset available which come from: P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553. ISSN: 0167-9236.
For more information visit the following link: https://s3.amazonaws.com/udacity-hosted-downloads/ud651/wineQualityInfo.txt
First, we load the necessary packages:
Then, we load the data:
To confirm the loading, we read the head of the data:
## fixed.acidity volatile.acidity citric.acid residual.sugar chlorides
## 1 7.4 0.70 0.00 1.9 0.076
## 2 7.8 0.88 0.00 2.6 0.098
## 3 7.8 0.76 0.04 2.3 0.092
## 4 11.2 0.28 0.56 1.9 0.075
## 5 7.4 0.70 0.00 1.9 0.076
## 6 7.4 0.66 0.00 1.8 0.075
## free.sulfur.dioxide total.sulfur.dioxide density pH sulphates alcohol
## 1 11 34 0.9978 3.51 0.56 9.4
## 2 25 67 0.9968 3.20 0.68 9.8
## 3 15 54 0.9970 3.26 0.65 9.8
## 4 17 60 0.9980 3.16 0.58 9.8
## 5 11 34 0.9978 3.51 0.56 9.4
## 6 13 40 0.9978 3.51 0.56 9.4
## quality
## 1 5
## 2 5
## 3 5
## 4 6
## 5 5
## 6 5Univariate Plots Section
Before plotting, we need to understand our data sets and its variables.
After a quick description and a summary, we will plot each variables.
Data set
## [1] 1599 12This tidy data set contains 1,599 red wines with 11 variables on the chemical properties of the wine, and 1 variable on the quality of the wine.
Regarding the quality variable, at least 3 wine experts rated the quality of each wine, providing a rating between 0 (very bad) and 10 (very excellent).
Noticed that our data is already tidy, so we do not have to clean it.
Variables
## [1] "fixed.acidity" "volatile.acidity" "citric.acid"
## [4] "residual.sugar" "chlorides" "free.sulfur.dioxide"
## [7] "total.sulfur.dioxide" "density" "pH"
## [10] "sulphates" "alcohol" "quality"Description of variables:
1 - fixed acidity: most acids involved with wine or fixed or nonvolatile (do not evaporate readily)
2 - volatile acidity: the amount of acetic acid in wine, which at too high of levels can lead to an unpleasant, vinegar taste
3 - citric acid: found in small quantities, citric acid can add ‘freshness’ and flavor to wines
4 - residual sugar: the amount of sugar remaining after fermentation stops, it’s rare to find wines with less than 1 gram/liter and wines with greater than 45 grams/liter are considered sweet
5 - chlorides: the amount of salt in the wine
6 - free sulfur dioxide: the free form of SO2 exists in equilibrium between molecular SO2 (as a dissolved gas) and bisulfite ion; it prevents microbial growth and the oxidation of wine
7 - total sulfur dioxide: amount of free and bound forms of S02; in low concentrations, SO2 is mostly undetectable in wine, but at free SO2 concentrations over 50 ppm, SO2 becomes evident in the nose and taste of wine
8 - density: the density of water is close to that of water depending on the percent alcohol and sugar content
9 - pH: describes how acidic or basic a wine is on a scale from 0 (very acidic) to 14 (very basic); most wines are between 3-4 on the pH scale
10 - sulphates: a wine additive which can contribute to sulfur dioxide gas (S02) levels, wich acts as an antimicrobial and antioxidant
11 - alcohol: the percent alcohol content of the wine
Output variable (based on sensory data): 12 - quality (score between 0 and 10)
data summary
## 'data.frame': 1599 obs. of 12 variables:
## $ fixed.acidity : num 7.4 7.8 7.8 11.2 7.4 7.4 7.9 7.3 7.8 7.5 ...
## $ volatile.acidity : num 0.7 0.88 0.76 0.28 0.7 0.66 0.6 0.65 0.58 0.5 ...
## $ citric.acid : num 0 0 0.04 0.56 0 0 0.06 0 0.02 0.36 ...
## $ residual.sugar : num 1.9 2.6 2.3 1.9 1.9 1.8 1.6 1.2 2 6.1 ...
## $ chlorides : num 0.076 0.098 0.092 0.075 0.076 0.075 0.069 0.065 0.073 0.071 ...
## $ free.sulfur.dioxide : num 11 25 15 17 11 13 15 15 9 17 ...
## $ total.sulfur.dioxide: num 34 67 54 60 34 40 59 21 18 102 ...
## $ density : num 0.998 0.997 0.997 0.998 0.998 ...
## $ pH : num 3.51 3.2 3.26 3.16 3.51 3.51 3.3 3.39 3.36 3.35 ...
## $ sulphates : num 0.56 0.68 0.65 0.58 0.56 0.56 0.46 0.47 0.57 0.8 ...
## $ alcohol : num 9.4 9.8 9.8 9.8 9.4 9.4 9.4 10 9.5 10.5 ...
## $ quality : int 5 5 5 6 5 5 5 7 7 5 ...## fixed.acidity volatile.acidity citric.acid residual.sugar
## Min. : 4.60 Min. :0.1200 Min. :0.000 Min. : 0.900
## 1st Qu.: 7.10 1st Qu.:0.3900 1st Qu.:0.090 1st Qu.: 1.900
## Median : 7.90 Median :0.5200 Median :0.260 Median : 2.200
## Mean : 8.32 Mean :0.5278 Mean :0.271 Mean : 2.539
## 3rd Qu.: 9.20 3rd Qu.:0.6400 3rd Qu.:0.420 3rd Qu.: 2.600
## Max. :15.90 Max. :1.5800 Max. :1.000 Max. :15.500
## chlorides free.sulfur.dioxide total.sulfur.dioxide
## Min. :0.01200 Min. : 1.00 Min. : 6.00
## 1st Qu.:0.07000 1st Qu.: 7.00 1st Qu.: 22.00
## Median :0.07900 Median :14.00 Median : 38.00
## Mean :0.08747 Mean :15.87 Mean : 46.47
## 3rd Qu.:0.09000 3rd Qu.:21.00 3rd Qu.: 62.00
## Max. :0.61100 Max. :72.00 Max. :289.00
## density pH sulphates alcohol
## Min. :0.9901 Min. :2.740 Min. :0.3300 Min. : 8.40
## 1st Qu.:0.9956 1st Qu.:3.210 1st Qu.:0.5500 1st Qu.: 9.50
## Median :0.9968 Median :3.310 Median :0.6200 Median :10.20
## Mean :0.9967 Mean :3.311 Mean :0.6581 Mean :10.42
## 3rd Qu.:0.9978 3rd Qu.:3.400 3rd Qu.:0.7300 3rd Qu.:11.10
## Max. :1.0037 Max. :4.010 Max. :2.0000 Max. :14.90
## quality
## Min. :3.000
## 1st Qu.:5.000
## Median :6.000
## Mean :5.636
## 3rd Qu.:6.000
## Max. :8.000Plots
We start by create a function to create histogram:
Fixed Acidity
The graph depicts the distribution of fixed acidity.
We can see that the distribution is right skewed.
Volatile Acidity
The graph depicts the distribution of volatile acidity.
We can see that the distribution is approximately normally skewed.
Citric acid
The graph depicts the distribution of citric acid.
We can see that the distribution is multi-modal.
Residual Sugar
The graph depicts the distribution of residual sugar.
We can see that the distribution is approximately right skewed.
Chlorides
The graph depicts the distribution of chloride.
We can see that the distribution is normal.
Free sulfur dioxide
The graph depicts the distribution of free sulfur dioxide.
We can see that the distribution is right skewed.
Total sulfur dioxide
The graph depicts the distribution of total sulfur dioxide.
We can see that the distribution is right skewed.
Density
The graph depicts the distribution of density.
We can see that the distribution is normal.
pH
The graph depicts the distribution of pH.
We can see that the distribution is normal.
Sulphates
The graph depicts the distribution of sulphates.
We can see that the distribution is right skewed.
Alcohol
The graph depicts the distribution of alcohol.
We can see that the distribution is right skewed.
## <ScaleContinuousPosition>
## Range:
## Limits: 0 -- 1The graph depicts the distribution of quality.
We can see that the distribution is normal.
Univariate Analysis
What is the structure of your dataset?
This data set contains 1,599 red wines with 12 columns.
11 numeric variables on the chemical properties of the wine and 1 quality variable.
What is/are the main feature(s) of interest in your dataset?
The main feature is the quality of the wine.
What other features in the dataset do you think will help support your
investigation into your feature(s) of interest?
Each numeric variables represent a charateristic of the wine, so each variables have to be investigating.
Did you create any new variables from existing variables in the dataset?
We did not have to create a new variable for the moment.
Of the features you investigated, were there any unusual distributions?
Did you perform any operations on the data to tidy, adjust, or change the form
of the data? If so, why did you do this?
The data set is already tidy, so we not have to perform any operations to tidy it.
Bivariate Plots Section
In this section, we will start by analysis the relation between quality and the other variables.
After, we will study interaction between features.
quality
In order to be able to create boxplots we need to make a discrete variable for quality.
We will do this in using cut:
Now we can create a function to make boxplots.
Finally, we can combine our functions.
Quality vs fixed.acidity
The median level of fixed acidity seems close between the quality rating. We can notice a sligh rise between 6 and 7.
Quality vs volatile.acidity
The volatile acidity is negatively correlated to the quality.
We can see a stability of the level of volatile acidity between 7 and 8, nevertheless this is not significant, because a small number of wines have a rating quality of 8.
Quality vs citric.acid
The citric acid is positively correlated with quality.
Quality vs residual sugar
The residul sugar seems weakly correalated to quality.
Quality vs chlorides
The chlorides seem weakly correalated to quality.
Quality vs free.sulfur.dioxide
Free sulfur dioxide and quality seem positively correlated between 3 and 5, then negatively correlated.
Quality vs total.sulfur.dioxide
Total sulfur dioxide and quality seem positively correlated between 3 and 5, then negatively correlated.
Quality vs density
Density seems to be negatively correlated with quality.
Nevertheless, we can notice a slight increase between 4 and 5.
Quality vs pH
PH seems to be negatively correlated with quality.
Quality vs sulphates
Sulphates and quality seems to be positevely correlated.
Quality vs alcohol
Alcohool and quality seems to be positevely correlated, except for the rate 5 which have a lot of ouliers.
Complementary bivariate plots
In order to choose some bivariate plots, we will start to create a correlation matrix, then visualize it.
We will plot the following bivariate correlations to get a better understanding:` - fixed acidity and citric acid - fixed acidity and density - fixed acidity and ph - free sulfur dioxide and total sulfur dioxide
Function to create scatterplot:
Fixed acidity vs citric acid
The scatterplot shows a linear positive moderate strong relation, nevertheless we can observe many outliers.
Fixed acidity and density
The scatterplot shows a linear positive strong relation.
Fixed acidity vs pH
scatter_plot(x = "fixed.acidity",y="pH")
The scatterplot shows a linear negative strong relation.
Free sulfur dioxide vs total sulfur dioxide
The scatterplot shows a linear positive strong relation.
Bivariate Analysis
Correlation summary:
Talk about some of the relationships you observed in this part of the
investigation. How did the feature(s) of interest vary with other features in
the dataset?
Quality is positevely correlated with alcohol and sulpates and negatively with volatile acidity.
The other relations are not sufficiently correlated.
Did you observe any interesting relationships between the other features
(not the main feature(s) of interest)?
We observed the following relations: - a positive correlation between acid citric and fixed acidity. - a positve correlation between density and fixed acidity. - a negative correlation between pH and fixed acidity. - a stong positive relation between free sulfur dioxide and total sulfur dioxide.
What was the strongest relationship you found?
The strongest relationship is between free sulfur dioxide and total sulfur dioxide, which is not really surprising, considering that total sulfur dioxide contains free sulfur dioxide.
Multivariate Plots Section
To ensure a better visibility, we create rating, a discrete variable, to gather quality by levels.
Funtion to create multivariate function:
Alcohol, volatile acidity and quality
We can observe that excellent wines tend to have lower volatile acidity over the entire range of alcohol.
Alcohol, volatile acidity and quality
We can observe that excellent wines tend to have higher sulphates over the entire range of alcohol.
Sulphates, volatile acidiy, quality
We can observe that excellent wines tend to have lower volatile acidity over the entire range of sulphates.
Fixed acidity, citric acid, quality
We can observe that excellent wines tend to have higher citic acid over the entire range of fixed acidity.
Fixed acidity, density, quality
We can observe that excellent wines tend to have lower density over the entire range of fixed acidity.
Fixed acidity, pH, quality
We do not see a clear pattern between the three variables.
Volatile acidity, density and quality
We can observe that excellent wines tend to have lower density over the entire range of volatile acidity.
We can also see that excellent wines are represented on a lower range of volatile acidity than medium and poor.
Free sulfur dioxide, total sulfur dioxide, quality
We do not see a clear pattern between the three variables.
Multivariate Analysis
Talk about some of the relationships you observed in this part of the
investigation. Were there features that strengthened each other in terms of
looking at your feature(s) of interest?
We principally explored the relationships which have the highest correlation with quality.
We observed that higher alcohol and lower volatile acidity means generally quality wines, that excellent wines tend to have higher citic acid over the entire range of fixed acidity.
We also observed that excellent red wines tend to have higher citic acid over the entire range of fixed acidity, and lower density over the entire range of volatile acidity.
Were there any interesting or surprising interactions between features?
Low density and low volatile acidity contain the better red wines.
Final Plots and Summary
Plot One
Description One
The graph depicts the distribution of acid citric in Red Wines.
This distribution wakes up our interest, because is the only muli-modal distribution among the variables.
Thus, it will be interesting to explore is relation with the quality variable.
Plot Two
Description Two
The box plot describes the relation between citric acid and quality of red wines.
Citric acid and qualiry of quaity of red wines have a postive correlation.
We can calcul the correlation to deepen our understanding of the two variables:
## [1] 0.2134809The correlation is positive but not really strong.
But it can be interesting to see the relation between citric acid and other variables.
Plot Three
Description Three
The multivariate graph represents the relation between citric acid, fixed acidity and quality.
We can observe that for each level of fixed acidity, excellent wines contain more citric acid than the two other categories (medium and poor).
Excellent wines conatin more citric acid by fixed acidity that poor wines.
Reflection
Our project was to explore the variables composing red wines, and particullary their impacts on the quality of red wine.
In a first time, we plotted each distributions variables. The vast majority of variable were right skewed, but we found out a mutivariate disbrution, citric acid, which attrated our curiosity. We also found out a limitation at our analysis: the distribution of the wines quality is largely composed by two rating: 5 and 6
In a second time, in order to not miss something, we plotted each variables with the quality variable. We discover the following cues: - a positive correlation with acid citric, alcohol and sulphates. - a negative correaltion with volatile acidity.
In parallel we observed the following relations: - a positive correlation between acid citric and fixed acidity. - a positve correlation between density and fixed acidity. - a negative correlation between pH and fixed acidity. - a stong positive relation between free sulfur dioxide and total sulfur dioxide.
At this step it was difficult to analyze the factor to a good wines, because the correlation between the variables and the quality was not strong enough.
In order to get a better understanding, we chosen a number of multivariate to plot. The most notables were: - Alcohol, volatile acidity and quality - Alcohol, volatile acidity and quality - Sulphates, volatile acidiy, quality - Fixed acidity, density, quality - Volatile acidity, density and quality
And of course Fixed acidity, citric acid and quality which ended our reflection on citric acid and quality.
We observed that for each level of fixed acidity, excellent wines containned more citric acid than the two other categories of red wines (medium and poor).
To improve our analysis we can collect more data, especially wines with a rating different of 5 and 6.
Ressources
Info on the data set: https://s3.amazonaws.com/udacity-hosted-downloads/ud651/wineQualityInfo.txt Plot: https://docs.google.com/document/d/1-f3wM3mJSkoWxDmPjsyRnWvNgM57YUPloucOIl07l4c/pub Histogram: http://www.sthda.com/english/wiki/ggplot2-histogram-plot-quick-start-guide-r-software-and-data-visualization#basic-histogram-plots Correlation: http://www.sthda.com/english/wiki/correlation-matrix-a-quick-start-guide-to-analyze-format-and-visualize-a-correlation-matrix-using-r-software Multivariate analysis: http://little-book-of-r-for-multivariate-analysis.readthedocs.io/en/latest/src/multivariateanalysis.html